AI Engineer

We’re Happy Scribe, a profitable, 100% bootstrapped AI company based in Barcelona.

We built one of the web’s most accurate multilingual transcription platforms, trusted by hundreds of thousands of users each month
. That product funds our freedom to create what’s next—no investors, no burn rate, just focus.

Now we’re taking that same tech and expertise to a bigger frontier:
turning conversations into intelligence. We’re building the layer where speech becomes memory, insights become action, and teams work with more clarity and speed.

You’d be joining a team of ~17 people, close-knit and pragmatic. Some of us are scaling a mature, revenue-generating platform. Others are prototyping new products that push the limits of speech and agentic AI
.

We move fast but think long-term. There’s room here to shape, ship, and own real products — not pitch decks.

We're looking for an AI Engineer to work on the AI layer that powers Happy Scribe Intelligence features. You will work on building features that bring together LLMs, speech models, and retrieval systems to address real user problems.

This role is for builders obsessed with making AI actually work — engineers who'd rather ship a clever prompt chain that solves a real problem than debate model architectures in Slack or over-engineer multi-agent frameworks that become quickly obsolete.

• Own AI features end-to-end — from problem discovery to prompt design to production. You'll build the systems that turn conversations into intelligence: summaries, search, actions, memory.

• Build and maintain our evaluation framework. You'll design evals that actually catch regressions, iterate on prompts with confidence, and keep quality high as models change under us.

• Optimize our AI infrastructure for speed, cost, and reliability. Provider routing, caching strategies, chunking approaches, latency budgets — you'll own the trade-offs.

• Stay dangerously current. The field moves weekly. You'll bring new techniques to the team and know when a new model or approach is worth the migration cost.

• Ship with the product team. You'll work closely with designers and engineers to integrate AI features that feel native, not bolted on.

• Example projects:

  • Design and ship semantic search across transcripts using embeddings and hybrid retrieval.

  • Build an agentic system that turns meeting notes into drafted emails, tasks, and follow-ups.

  • Create a prompt evaluation pipeline that catches quality regressions before users do.

• You're a seasoned engineer (5+ years) who's spent the last 2+ years deep in AI applications. You’ve shipped LLM-based features to production — not demos, not notebooks, real products with real users.

• You stay current by doing, not just reading. New model drops Tuesday, you've tested it by Thursday, and you know whether it's worth a migration or just hype.

• You're fluent in prompt engineering, RAG architectures, and evaluation frameworks. You've debugged retrieval that returned garbage and prompts that hallucinated confidently.

• You think in trade-offs: latency vs. quality, cost vs. capability, speed-to-ship vs. technical debt. You make calls and move.

• You're product-minded. You care more about solving user problems than implementing cool tech. You'll kill a feature that doesn't work.

• You are excited about our engineering principles and most of the Rails doctrine clicks with your style of work.

• You've shipped AI features in B2B SaaS where reliability and accuracy actually matter.

• You've built multi-provider LLM systems with fallbacks, routing, and cost optimization.

• You've designed retrieval systems at scale — embeddings, reranking, hybrid search.

• You have experience with speech or audio AI — transcription, diarization,…